Deciphering chemical diversity among five variants of Abeliophyllum distichum flowers through metabolomics analysis

Abstract Abeliophyllum distichum (Oleaceae), endemic to the Korean Peninsula and the sole member of its genus and species, possesses high scarcity value, escalating its importance under the Nagoya Protocol. Despite its significance, their metabolites and activities of A. distichum flowers remain unexplored. This study employs an integrated metabolomic approach utilizing NMR, LC/MS, GC/MS, and FTIR techniques to comprehensively analyze the metabolite profile of A. distichum flowers. By combining these methods, we identified 35 metabolites, 43 secondary metabolites, and 108 hydrophobic primary metabolites. Notably, distinct concentration patterns of these compounds were observed across five variants, classified based on morphological characteristics. Correlation analyses of primary and secondary metabolites unveiled varietal metabolic flux, providing insights into A. distichum flower metabolism. Additionally, the reconstruction of metabolic pathways based on dissimilarities in morphological traits elucidates variant‐specific metabolic signatures. These findings not only enhance our understanding of chemical differences between varieties but also underscore the importance of considering varietal differences in future research and conservation efforts.


| INTRODUCTION
Protection of the sovereignty of biological resources in each country is important due to the recent Nagoya protocol (Kamau et al., 2010).
The purpose of this protocol is the fair and equitable sharing of benefits arising from the use of genetic resources, which contributes to the conservation and sustainable use of biodiversity.Research using domestic native species became a key issue with this protocol in force.
At present, there are only six plants (Abiliophyllum distichum plants in Korea that are monotaxons.Among the various domestic native plants, A. distichum Nakai (Oleaceae), known as white forsythia, is both one species and one genus and is grown only on the Korean Peninsula (Lee, 1983;Park et al., 2019).Due to its high scarcity and ecological and geographic value, the study of A. distichum has rarely been carried out compared with other plants.Previous phytochemical studies of these plants have focused on just the leaves, and they reported that four phenylethanoid glycosides and two flavonoids are components of these plants (Kuwajima et al., 1993;Li et al., 2013;Oh et al., 2003).In 2017, A. distichum was deleted from the list of endangered species by the Ministry of the Environment (Ministry of Environment, 2017) due to the development of a mass breeding technique (Lee et al., 2014(Lee et al., , 2015)).
At present, five variants of this plant have been reported: white miseon (A.distichum Nakai), pink miseon (A.distichum for.lilacinum Nakai), ivory miseon (A.distichum for.eburneum T. B. Lee), blue miseon (A.distichum for.viridicalycinum T. B. Lee), and round miseon (A.distichum var.rotundicarpum T. B. Lee) (Lee, 1976;Nakai, 1919Nakai, , 1922)).The variants were classified based only on morphological characteristics like the color of the petals and sepals or the shapes of the fruit (Figure 1).There are many opinions on the taxonomic identities of this plant, and some documents even suggest that each variant had the same taxa (Kim et al., 2002).Accordingly, phytochemical investigations and chemical maps for the variants should be valuable.
In this study, metabolomics was applied to estimate the dissimilarities in the chemical compositions of the variants of A. distichum flowers to understand the dissimilarities of the morphological characteristics.NMR spectroscopy, LC/MS, GC/MS, and FTIR were used as the main metabolomic tools.
Compared with other platforms, LC/MS is the most suitable platform for metabolomic studies of secondary metabolites (Bin et al., 2012;Rogachev & Aharoni, 2012).Accordingly, chemical mapping for classification using LC/MS on this plant will provide very useful data for understanding how classification in A. distichum could be expressed and associated with its morphological characteristics.
However, LC/MS-based metabolomics is limited to secondary metabolites, and it was difficult to analyze the whole metabolome of five variants of A. distichum.In organisms, primary metabolites such as nucleotides, amino acids, organic acids, and sugars are directly related to the development, growth, and reproduction of cells, and so on (Kind et al., 2009).Thus, analysis of primary metabolites of this plant using another platform can help us to understand how metabolic flux is related to its morphological characteristics.There are many analytical methods for analyzing primary metabolites such as GC/MS, NMR, and CE/MS, along with others (Emwas et al., 2019).Among them, the GC/MS platform has many advantages, such as superior sensitivity, robustness, and reproducibility for small and hydrophilic molecules, including primary metabolites (Jonsson et al., 2006).Also, GC/MS-based metabolomics that focuses on hydrophobic primary metabolites cannot measure all primary metabolites (especially the more polar ones).
NMR spectroscopy is one of the most powerful techniques for identifying absolute chemical structures using their chemical shifts, coupling constants, and coupling patterns (Smolinska et al., 2012).
Moreover, intensities in NMR data are proportional to the molecular concentration, making quantification of all compounds possible even without calibration curves of each metabolite (Simmler et al., 2014).Specifically, NMR is able to reflect the real molecular concentrations of metabolites present in the whole organism.For this reason, NMRbased metabolomics is used to determine the metabolites in numerous plant and animal sources in extracts and live organisms without purification and separation (Beger, 2013;Bharti et al., 2011;Cuperlovic-Culf & Culf, 2016;Jia et al., 2015).Due to these advantages, metabolomic research using NMR has steadily increased over the past 15 years (Emwas et al., 2019).However, the application of NMR in the metabolomic study has some disadvantages, such as a lack of sensitivity and difficulty identifying overlapping peaks (Takis et al., 2019;Kim, Choi, & Verpoorte, 2010).However, there are some solutions to the shortcomings of NMR to a certain degree.A lack of sensitivity of NMR analysis can be improved with multiple scans and higher magnet field strengths.Overlapping peaks can be resolved using various 2D NMR techniques such as COSY, TOCSY, DOSY, NOESY, J-resolved, HSQC, and HMBC (Emwas et al., 2019).The metabolites of the flowers in five variants of A. distichum flowers were identified using UHPLC-TripleTOF-ESI-MSMS analysis.Among the various extracted conditions of A. distichum flowers (50% to 100% MeOH), 80% aqueous MeOH was selected as the best extraction solvent in preliminary experiments (data not shown).UHPLC equipment and a 1.6-μm particle size column was used for chromatographic analysis under a short analytical time for secondary metabolites.The solvent gradient elution was set as described in the Material and Method section to separate each metabolite effectively.Both ion modes (negative and positive) were tested for electrospray ionization (ESI) instruments (data not shown).Among them, all components of A. distichum flowers showed higher sensitivity in negative ion mode.
In this study, LC/MS spectra were recorded in quadruplicate.
Figure 2 shows the representative total ion chromatograms (TIC) of different metabolites from five variants of A. distichum flowers.The intensities of peaks, especially those eluted from 6 to 8 min, were significantly different depending on each morphological characteristic.
For instance, variants of colorless petals on white miseon and round miseon flowers showed similar aspects in each chromatogram.Chromatograms of blue miseon, which has blue sepals, showed different aspects compared with the others.Chromatograms of pink miseon showed strong intensities of each peak eluted from 6 to 8 min.These chromatograms indicated that each variant had different metabolic compositions based on their morphological characteristics, such as the color of the petals and sepals and the shape of the fruit.
Identification of each component was carried out based on a comparison of mass data for each peak with those in the NIST Library, HMDB, and MoNA export LC-MS, MS-MS Library; 250 components identified with scores higher than 0.8 were separated using UHPLC-TripleTOF-ESI-MSMS in 15 min.Among them, 43 metabolites were selected according to the mass accuracy of each peak (values smaller than 7 ppm) as well as literature, isotopic pattern, and MSMS patterns (Table 1).

| Profiling of primary metabolites from the flowers in five variants of A. distichum using GC-MS analysis
The identification of hydrophobic primary metabolites was run in triplicates based on the TIC from the measurement of five variants of A. distichum flowers using GC coupled with triple quadrupole-mass spectrometry (QqQ/MS).Figure 3 shows the representative TIC of diverse primary metabolites emitted from five variants of A. distichum flowers.The intensities of the peaks were significantly different depending on the sample, indicating that each variant had different metabolic compositions as well as different morphological characteristics.Each peak was identified by matching their spectra with a NIST library and published literature as well as analyzing the retention indices calculated against n-alkanes (C 7 -C 30 ) (i.e., retention time and relative retention time).They were confirmed through analysis of fragmentation patterns in mass spectra.Sugar derivatives, such as hexose-type sugars, pentose-type sugars, and sugar alcohol, were gauged by the retention index (RI) values in the literature (Choi et al., 2013;Medeiros & Simoneit, 2007;Wagner et al., 2003;Xia et al., 2018).
A total of 108 primary metabolites were identified and quantified in GC/MS analysis of five variants of A. distichum flowers, including seven organic alcohols and their aldehydes, 11 organic acids, 20 amino acids, four amino alcohols, five nucleic acids, 11 fatty acids, 31 sugars, including its alcohols and acids, and five others.RT, relative RT (RRT), RI, quantification ion (QI), and mass fragments of analytes are illustrated in Table 2.

| Profiling of primary metabolites from five variants of the flowers in five variants of A. distichum using NMR analysis
The primary metabolites in five variants of A. distichum flowers were identified using NMR analysis.Six kinds of deuterated solvents (D 2 O, CD 3 OD, DMSO-d 6 , acetone-d 6 , pyridine-d 5 , and CDCl 3 ) are commonly used in NMR experiments.These solvents have different polarities, and solvents are chosen based on the solubility of the target component.Preliminary experiments were performed using these deuterated solvents to identify the most suitable extract conditions to provide samples high in primary metabolites.Among the various extracts using several deuterated NMR solvents of A. distichum flowers (Figure S1), D 2 O and CD 3 OD, which are highly polar solvents, effectively extracted primary metabolites such as organic acids, sugar, and amino acids in the preliminary experiments.Thus, samples were extracted and analyzed with deuterated 50% aqueous methanol to obtain comprehensive NMR spectra of the metabolites of A. distichum flowers.Figure 4 shows the representative 1 H-NMR spectra of each variant, which showed significant differences depending on each morphological characteristic.Further confirmation of each metabolite in the extract was achieved using 2D spectra  (Abbas et al., 2018;Dai et al., 2010;Grauso et al., 2019;Kim, Choi, & Verpoorte, 2010;Kumar et al., 2019;Li et al., 2018;).We identified 35 metabolites, including amino acids, nucleic acids, organic acids, secondary metabolites, and sugars.These are shown in the three main regions of a representative 1 H-NMR spectrum of A. distichum flowers in Figure 5 and Table 3.     T A B L E 2 The primary metabolites identified from the flowers in five variants of Abeliophyllum distichum using GC-MS analysis.Although the score plots of PCA/PLS-DA results provide some idea of grouping, the available principal components are limited because only three of them can be graphically presented.Also, the score plots do not provide any information on the similarities between groups (Kim, Choi, & Verpoorte, 2010;Kim, Saifullah, et al., 2010).Therefore, we constructed a dendrogram for hierarchical cluster analysis (HCA) to reveal the similarities between the five variants of A. distichum flowers using 43 metabolites obtained from LC/MS results.As shown in Figure S9, white miseon and round miseon, which have white petals and similar shapes, showed higher similarity than the other variants.
Ivory miseon, which has ivory petals, was clearly different from the other variants.These results indicate that various secondary metabolites (i.e., phenylethanoid glycosides, flavonoids, phenyl ethanoid, and phenyl propanoids) had a major influence on chemical mapping and helped us to understand the morphological characteristics.

| Multivariate data analyses of metabolites using GC/MS
Before the multivariate data analyses, each peak identified as a primary metabolite (Table 2) was normalized to fluoranthene as an internal standard.The PCA and PLS-DA results show distinct separations between all five variants of A. distichum flowers, indicating that each primary metabolite was related to the phenotype of each variant.The PCA result derived from components of each variant is shown in the score plot (Figure 7) together with the loading plot (Figure S10), constituting the first principal component (PC 1, 50.3%) and second principal component (PC2, 18.9%).These described 69.2% of the total variance in the optimal separation of data.Variants based on different colors of sepals or fruit shapes (blue miseon and round miseon) and others were clearly segmented by the PC1 and by the PC2, where pink and blue miseon were clearly separated from the others.The loading plots (Figure S10) of PCA results were consistent with their respective score plots, which described the dissimilarities of hydrophobic primary metabolite characteristics based on their correlative clusters, highlighting variations in their fingerprints.Based on the PC1 in the loading plot, blue miseon and round miseon, which have different fruit shapes or sepal colors showed higher contents of seven amino acids (L-isoleucine, L-valine, L-serine, L-glutamine, L-proline, L-alanine, and GABA), four hexose types of sugars (glucose, mannose, fructose, and gentiobiose), two sugar derivatives (myo-inositol and quinic acid), and one organic acid (malate) than others.On the other hand, one amino acid (L-serine), one organic acid (malonic acid), seven 6-deoxyhexose or pentose types of sugars (arabinose, ribose, rhamnose, lyxose, fucose, and sucrose), three sugar alcohols (galactinol, ribitol, and mannitol), and four fatty acids (stearic acid, palmitic acid, and linoleic acid) were high in white, pink, and ivory miseon compared with other variants.On the basis of PC2 in the loading plot, glycerol, two organic acids (succinic acid and phosphoric acid), four sugars (mannose, fructose, glucose, and glucuronic acid), three sugar alcohols (ribitol, galactinol, and myo-inositol), and two fatty acids (palmitic acid and stearic acid) were high in white, ivory, and round miseon compared with others that have different colors of petals (pink miseon) or sepals (blue miseon).Otherwise, one amino acid (L-proline), one organic acid (malate), and two sugars (gentiobiose and arabinose) were highly concentrated in blue and pink miseon.PLS-DA analysis was performed to further elucidate the segregation of primary metabolites from each variant, especially white and ivory miseon (Xia et al., 2017).
PLS-DA results (Figures S11 and S12) show a total variance of 68.6% and showed many of the same aspects as the PCA results.This alignment indicates that primary metabolite fingerprints are deeply related to the morphological characteristics of A. distichum flowers such as the fruit shape and color of sepals and petals.

| Multivariate data analyses of metabolites using NMR
The metabolite fingerprints of five variants of A. distichum flowers (white miseon, pink miseon, ivory miseon, blue miseon, and round miseon) (Figure 8) were obtained with PCA, a method of unsupervised multivariate projection, to effectively describe the dissimilarities based on the NMR data patterns (Jonsson et al., 1991).Mathematical methods of scaling and mean-centering the data set to unit variance resulting from the above samples were performed by the SIMCA-P T A B L E 3 The metabolites identified from the flowers in five variants of Abeliophyllum distichum using NMR analysis.5.19 (d, 3.6), 3.81, 3.70, 3.47, 3.37, 3.19 (dd, 8.4, 7.8) β-Glucose 4.60 (d, 7.8), 3.87, 3.70, 3.42, 3.37, 3.19 (dd, 8.4  the peak area normalization method was conducted to determine the relative amounts using an internal standard.RI were calculated using the following equation (Kováts, 1958) Here, x is the targeted compound, n is the number of carbon atoms of the n-alkane eluted before x.n + 1 is the number of carbon atoms of the n-alkane eluted after x, t x is the retention time of x, t n is the retention time of n, and t n + 1 is the retention time of n + 1.
Raw chromatographic data acquired from triple quadrupole GC/MS analysis were processed using Xcalibur 3.1 software (Thermo Finnigan Corporation, San Jose, CA, USA), in which automatic peak detection and mass spectrum deconvolution (compound identification) were performed with references to library NIST 2.0.

For
this reason, there is no single tool used to analyze whole metabolites.In this study, we used a combination of NMR, LC/MS, and GC/MS for the complete identification of all metabolites, and quantification platforms were used for chemical mapping to determine the dissimilarities and chemical compositions of the metabolites F I G U R E 1 Appearances of Abeliophyllum distichum flowers.(to reveal their metabolic flux) and reconstruct a metabolic pathway for understanding the dissimilarities in the morphological characteristics for the flower in five variants of A. distichum.2 | RESULTS AND DISCUSSION 2.1 | Profiling of metabolites from the flowers in five variants of A. distichum using UHPLC-TripleTOF-ESI-MSMS analysis

2. 4 |
Multivariate data analyses of metabolites using LC/MSTo understand how the classification of the flowers in five variants of A. distichum could be expressed and correlated to morphological characteristics, multivariate data analyses were performed on the metabolites identified in Materials and Methods.The metabolite fingerprinting of five variants of A. distichum flowers (Figure6) was carried out with PCA, an unsupervised multivariate pattern recording method, to visualize the dissimilarities based on the chromatographic pattern efficiently.Mean-centered and par-scaled (scaled to the square root of SD) mathematical methods were performed to pretreat the data sets resulting from the above samples using the SIMCA-P 14.1 software.The PCA and PLS-DA results show a distinct separation between all five variants of A. distichum flowers.The PCA score plots (Figure6) representing an analysis derived from the negative ionization mode describe 66.7% of the total variance in which optimal segregation was achieved between the principal component 1 (PC1, 46.4%) and the principal component 2 (PC2, 20.3%), where PC1 was the key component for sample separation.Pink miseon and blue miseon were clearly separated from the others by PC1.Also, white miseon and round miseon, which have white petals and purple sepals, were clearly separated from the others by the PC2.The loading plots (FigureS7) and biplot (FigureS8) of the PCA results were consistent with their respective score plots, in which significant intensities of each metabolite variable for their correlative clusters reflected variations in their metabolite fingerprints.Based on PC1 in loading plots, pink miseon, which has unique petal colors, and blue miseon, which has a blue color on the sepals, showed higher content of lignans ((À)-pinoresinol di-O-β-D-glucopyranose, (À)-8-hydroxypinoresinol 4-O-β-D-glucopyranose, and pinoresinol 4-O-glucoside), phenylethanoid glycosides (echinacoside, forsythoside F, and acteoside), and phenolic glucosides (1-O-(E)-caffeoyl-β-D-glucopyranose, and T A B L E 1 (Continued)

1 -
O-(E)-phenylethyl-β-D-glucofuranosyl-O-(1 !2)-β-D-glucopyranoside) than others.On the other hand, flavonoids (calendoside III, rutin, nicotiflorin, and naringenin) were more prevalent in pink miseon and blue miseon compared with the others.The PC2 loading plot shows that all secondary metabolites were higher in pink and ivory miseon, which have colored petals, and blue miseon, which have different colors on the sepal, as compared with white miseon and round miseon, which have white petals and purple sepals.The biplot (Figure S8) of the PCA results also showed the distribution of markers in variants of A. distichum flowers.The preferential distribution of flavonoids in the first quadrant of the biplot primarily accounted for the differences in ivory miseon.Also, phenylethanoid glycosides, lignans, and phenolic glucosides distributed in the second quadrant of biplots account for the variations in pink and blue miseon.Otherwise, no secondary metabolite T A B L E 2 (Continued) was distributed in the third and fourth quadrants of the biplots, accounting for the variations in white and round miseon.This alignment indicates that variations in secondary metabolite fingerprints have an effect on the morphological characteristics of A. distichum flowers such as the shape of the fruit and the colors of the sepals and petals.

14. 1
software.Before multivariate data analyses, each peak of the identified primary metabolites (Figure5) was normalized to TSP as an internal standard.The PCA and PLS-DA results show distinct separations between all five variants of A. distichum flowers, indicating that metabolites were related to the phenotype of each variant.The PCA result derived from the components of each variant shows the score plot (Figure8) together with the loading plot (FigureS13), constituting the first principal component (PC 1, 54.2%) and second principal component (PC2, 24.0%).These described 78.2% of the total variance in which the data were optimally separated.White miseon and round miseon, which have the same colors of petals and sepals, and others were clearly distinguished by the PC1 and by the PC2.Blue miseon and round miseon were also clearly separated from the others.The loading plots (FigureS13) of PCA results were consistent with their respective score plots, in which the described dissimilarities of primary metabolic characteristics from their correlative clusters were observed by variations in their fingerprints.Based on PC1 in the loading plot, white miseon and round miseon showed higher contents of sugars (α-glucose, β-glucose, and α-galactose) and amino acid (alanine) than others.On the other hand, more amino acids (glutamine, proline, arginine, threonine, and leucine) and amino alcohols (choline) were contained in pink, ivory, and blue miseon compared with white F I G U R E 6 PCA score plot obtained from LC-MS data on five variants of Abeliophyllum distichum flowers.(a) White miseon, (b) pink miseon, (c) ivory miseon, (d) blue miseon, (e) round miseon.F I G U R E 7 PCA score plot obtained from GC/MS results on five variants of Abeliophyllum distichum flowers.(a) White miseon, (b) pink miseon, (c) ivory miseon, (d) blue miseon, (e) round miseon.nebulizing gas at 3.44738 bar, a heating gas at 3.44738 bar, and a curtain gas at 1.72369 bar.The desolation temperature was 500 C, the ion spray voltage was 4.5 kV, the collision energy was À35 ± 15 eV, and the collision gas was N 2 .The mass range was set at m/z 100-2000 Da for MS scans and MS/MS scans.Raw chromatographic data acquired from LC/MS analysis were processed by PeakView 2.2 (SCIEX, Framingham, MA, USA) and Scafford Elements version 2.2.1 (Proteome Software, Inc, Portland, OR, USA), in which automatic peak detection and mass spectrum deconvolution were performed with references to the NIST Library (ver.2017 for Elements), the Human Metabolome Database (HMDB, http://www.hmdb.ca),and the MoNA export LC-MS, MS-MS Library.3.3 | Gas chromatography-mass spectrometry (GC-MS) analysisEach sample (50 mg) and 100% MeOH (1 mL) in a 2-mL Eppendorf tube was ultrasonically extracted for 30 min at 37 C and centrifuged (Smart R17, Hanil Science Industrial Co., Ltd., Seoul, Korea) at 14,000 rpm under 4 C for 10 min.Then, 100 μL of supernatant was transferred to another 2-mL Eppendorf tube.The extract was dried in a vacuum centrifuge dryer at 20,000 rpm under 4 C until it was dry.For derivatization, 30 μL of methoxyamine hydrochloride in pyridine (20 mg/mL) was added as the first derivatizing agent.The mixture was incubated in a water bath at 30 C for 90 min.A second derivatizing agent, 50 μL of BSTFA containing 1% TMC, was added and incubated in a dry oven at 70 C for 30 min.Then, 5 μL of fluoranthene (500 ppm in pyridine) was added in each sample as an internal standard (Figure S14.).All chemicals used in this study were analytical grade.Methanol was used as an extraction solvent, fluoranthene (diluted with pyridine to a concentration of 0.5 mg/mL), n-alkane standards (C 7 -C 30 ), and pyridine were purchased from Sigma-Aldrich Chemical Co.(WO, USA).Methoxyamine hydrochloride and N,O-bis(trimethylsilyl) trifluoroacetamide (BSFTA) was purchased from Sigma (WO, USA).A Thermo Scientific Trace™ 1300 (Thermo Fisher Scientific Inc., USA) gas chromatography (GC) instrument was used in this study to identify primary metabolites of A. distichum flowers.The primary metabolites were separated using a DB-5 MS column (60 m Â 0.25 mm internal diameter Â 0.25 μm film thickness).Helium was used as the carrier gas at a constant flow rate of 1.5 mL/min.The oven temperature condition started at 50 C for 2 min, and then it was programmed to increase from 50 C to 180 C at a rate of 5 C/min, then it was held for 8 min, then heated from 180 C to 210 C at a rate of 2.5 C/min, and then from 210 C to 325 C at a rate of 5 C/min and was finally held for 20 min.A 1-μL sample was injected with a 20:1 (v/v) split ratio.The GC-MS transfer line temperature was set at 300 C. The triple quadrupole mass spectrometer (TSQ 8000, Thermo Fisher Scientific Inc.) operated in scan mode at 70 eV, and the electron ionization (EI) source was kept at 270 C. Two scans per second were recorded over the mass range m/z of 35-650 Da.The identification of metabolites was confirmed by comparing their spectra and retention indices (RI) with standards from the NIST 2.0 library.Quantitative analysis by

Fifty
milligrams of each ground sample and 1.5 mL of a 1:1 mixture of deuterated methanol (MeOD) and deuterated water (D 2 O) in a 2 mL Eppendorf tube were vortexed for 1 min.After vortexing, each tube was ultrasonically extracted for 20 min at 30 C and centrifuged(Smart R17, Hanil Science Industrial Co., Ltd., Seoul, Korea)  at 15,000 rpm and 4 C for 15 min.Then, 750 μL of each supernatant was transferred into Norell ® Standard Series™ 5 mm NMR tubes (for 600 MHz, Norell, Inc. Mays Landing, NJ, USA) for NMR measurements (Figure S15.).D 2 O [99.9% atom% D, contains 0.05% (v/v) 3-(trimethylsilyl)-propionic-2,2,3,3-d 4 acid sodium salt (TSP) as an internal standard], methanol-d 4 (99.8 atom% D), and other deuterated NMR solvents (99.8 atom% D) were purchased from Sigma Aldrich Co. Ltd (St. Louis, MO, USA).1D ( 1 H-and 13 C-) and 2D (COSY, HSQC, HMBC, TOCSY, and J-reserved) NMR spectra of A. distichum flowers were recorded on a Bruker Advance 600 spectrometer (Billerica, MA, USA) operating at 600.13 MHz with a 5-mm TXI probe at 298 K.A presaturation pulse sequence of all 1D NMR spectra with zg30 without residual H 2 O suppression via low power selective irradiation of the H 2 O frequency during mixing and recycle delay were acquired.Measurements were made using a 20.03 ppm (12019.23Hz) spectral width, a 5.0-s repetition time, and a pulse width of 90.74 (flip angle).Thirty-two transients were collected with 63,536 data points at an acquisition time of 2.73 s.Two dummy scans were made prior to the 128 recorded scans.The free induction decays (FIDs) were Fourier transformed with a line-broadening function of 0.37 Hz.The baselines of 1 H-and 13 C-NMR spectra were manually referenced to the internal standard (TSP, δ H and δ C 0.00 ppm).The COSY spectra with a pulse sequence of cosydfgpph19 were acquired with a covering spectral width of 13.02 ppm (7812.50Hz) in both dimensions.The data matrix had 2048 Â 128 points, there were 32 transients per increment at an acquisition time of 0.13 s.Eight dummy scans were obtained prior to 64 recorded scans.The sine bell-shaped window function (SSB = 2.0) was applied for COSY spectra processing.The TOCSY spectra with a pulse sequence of mlevphpp were acquired with a spanning spectral each metabolomic platform (i.e., LC/MS, GC/MS, and NMR).Schemes of metabolic pathways were revealed according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg).According to the shikimate pathway (Figure 9), tyrosine plays a significant role in the biosynthesis of phenolic glycosides, phenylpropanoid glycosides, and lignans.Among them, phenolic glycosides (1-O-(E)phenylethyl-β-D-glucofuranosyl-O-(1 !2)-β-D-glucopyranoside and 1-O-(E)-caffeoyl-β-D-glucopyranose) is contained in a large quantity in blue miseon.This indicates that phenolic glycosides are related to the color of the sepals.Phenylpropanoid glycosides and lignans were deeply related to the morphological characteristics of pink and blue miseon.Variants having colored petals showed high concentrations of amino acids (tryptophan, phenylalanine, and tyrosine) derived from the shikimate pathway.On the other side, alanine derived from pyruvate (the next step in the shikimate pathway) showed low concentrations in colored variants.Also, phenylalanine derived from shikimic acid was related to the biosynthesis of flavonoids.Naringenin, a precursor of flavonoids, was significant in ivory miseon.This flavanone was associated with yellowish pigments in these flowers.Also, flavonol glycosides, which are the next step in the flavonoid biosynthesis of naringenin, were highly involved in colored variants (ivory and pink miseon).These components were deeply related to the colors of the petals.Serine concentration was relatively higher in round miseon than in other variants.Therefore, round miseon contains relatively low contents of both acetate and organic acids and amino acids derived from the tricarboxylic acid (TCA) cycle.Therefore, we proposed that the content of serine and organic acids and amino acids derived from TCA cycle were related to fruit shape.The extensive integration of A. distichum metabolic networks revealed by metabolomics flatforms allowed us better to understand the chemical compositions and dissimilarities in morphological characteristics.AUTHOR CONTRIBUTIONS Yeong-Geun Lee, Nam-In Baek, and Se Chan Kang designed the research; Yeong-Geun Lee, Jeong Eun Kwon, and Won-Sil Choi performed the experiments; Yeong-Geun Lee, Jeong Eun Kwon, Won-Sil Choi, and Nam-In Baek analyzed data; and Yeong-Geun Lee wrote the paper.All authors contributed to the article and approved the submitted version.